Use Cases
examples of how ai can strengthen journalistic work
AI shows up in journalism in many small ways before it shows up in big ones: transcribing interviews, sorting documents, drafting headlines, translating text. This card collects concrete examples so a team can see where AI might fit their own work. Looking at real tasks keeps the discussion grounded rather than abstract.
Questions to explore
- Which repetitive tasks in your work could AI take on without touching editorial judgment?
- Where has AI already saved time for someone on your team, and how?
- Which use cases would you not trust to AI, and what makes them different?
- How would you test whether a use case actually improves the work or just speeds it up?
- What new kinds of stories or coverage might these tools make possible for you?
Expert voices
“Map use cases across newsgathering, production, and distribution, and reflect on three levels: the individual journalist, the newsroom, and the audience.”
“Ask early whether AI sits in the background or the foreground: do you use it internally, or does your audience interact directly with AI-generated content?”
“AI enables investigative tools that were out of reach a few years ago. The New York Times trained a model to detect bomb craters, showing one of Israel's biggest bombs was used routinely in south Gaza.”
Things to consider
- Start with low-risk tasks where mistakes are easy to catch.
- Faster is not always better if it adds review work later.
- A use case that fits one newsroom may not fit yours.
Pull Use Cases when it is relevant and set it aside when it is not. Pair it with the other AI Conversations cards, lay them out on a table, and use the questions above to get everyone on the same page. Capture what you discuss on sticky notes or in a shared doc.
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